Jean-Luc Jannink
0000-0003-4849-628X
USDA-ARS
148 papers found
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Simulation of sugar kelp (Saccharina latissima) breeding guided by practices to accelerate genetic gains
Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)
Sexual dimorphism and the effect of wild introgressions on recombination in cassava (Manihot esculenta Crantz) breeding germplasm
Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation
RNA polymerase mapping in plants identifies intergenic regulatory elements enriched in causal variants
Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages
Recurrent genomic selection for wheat grain fructans
Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed
Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots
Genome wide association study of 5 agronomic traits in olive (Olea europaea L.)
Historical Introgressions from a Wild Relative of Modern Cassava Improved Important Traits and May Be Under Balancing Selection
Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.)
Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance
A framework for genomics-informed ecophysiological modeling in plants
Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection
High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage
Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid
Influence of Genotype and Environment on Wheat Grain Fructan Content
A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data
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